This invention relates to a method of estimating weather risks, typically, risks in weather derivatives.
Weather conditions such as temperature, precipitation, and the amount of snow cover significantly affect business activities of a company. For instance, a heat wave in summer boosts sales for air conditioning manufacturers and electric power companies whereas it dents profit of department stores and railway companies by raising cooling cost. To give another example, extraordinarily low precipitation brings more customers or visitors to travel agencies and theme parks whereas it is adverse to electric power companies since their hydraulic power units cannot run efficiently and the cost of alternative power generation increases. Fluctuations in sales or profit due to such weather conditions are called weather risks.
In recent years, financial derivatives called weather derivatives are attracting attention as an instrument to reduce weather risks and ensure a steady profit. A weather derivative is a contract between a business entity subject to weather risks and a property insurance company or the like in which the business entity pays the insurance company contract money first and receives a compensation payment if certain set weather conditions are met in future times. Several methods have been proposed to calculate contract money of a weather derivative (refer to JP 2001-222605 A and JP 2003-122918 A).
Proposed as the basis for calculating contract money of a weather derivative are a weather forecast based on a multi-site temperature model (refer to “Multivariate long memory modeling of daily surface air temperatures and the valuation of weather derivative portfolios”, written by Rodrigo Caballero et al., internetURL: http://stephenjewson.com/articles/), a weather forecast based on a multi-site precipitation model (refer to “Multisite generalization of a daily stochastic precipitation generation model”, D. S. Wilks, Journal of Hydrology, 1998, 210, pp. 178–191), and a weather forecast based on a single-site precipitation-temperature correlation model (see, for example, Richardson, C. W., “Stochastic simulation of daily precipitation, temperature, and solar radiation”, Water Resources Research, 17, pp. 182–190).
A provider of a weather derivative (a property insurance company, a trade firm, or a bank) holds a portfolio of the contract, and needs to calculate the amount of risk and analyze factors about the portfolio. Conventionally, those needs have been taken care of in the following manner. First, a time-series model built for each site or for each meteorological element is used to create a weather scenario. The 99% VaR (Value at Risk) is calculated from the weather scenario created and the sum of the 99% VaR is evaluated as the total amount of risk (FIG. 12).